Deep-learning in survival analysis

In this paper, hospitalisation duration is modelled using traditional survival model, machine-learning models and deep-learning models. Machine-learning and deep-learning algorithms typically assume that all event of interest are known at the time of modelling. However, hospitalisation duration i...

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Main Author: Ho, Jeff
Other Authors: Xiang Liming
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/148490
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1484902023-02-28T23:12:56Z Deep-learning in survival analysis Ho, Jeff Xiang Liming School of Physical and Mathematical Sciences LMXiang@ntu.edu.sg Science::Mathematics::Statistics In this paper, hospitalisation duration is modelled using traditional survival model, machine-learning models and deep-learning models. Machine-learning and deep-learning algorithms typically assume that all event of interest are known at the time of modelling. However, hospitalisation duration is a time-to-event data with right-censoring (not all events are known at the time of modelling). Hence, specific techniques were employed to deal with this inconsistency. Subsequently, the various models are evaluated using the Concordance-index (C-index). It is a ranking evaluation metrics that can account for censored observation. The empirical results showed that the deep-learning model is best in predicting hospitalisation despite the small dataset. This paper can be further improved by incorporating geo-spatial data in the analysis. Bachelor of Science in Mathematical Sciences and Economics 2021-04-28T02:10:24Z 2021-04-28T02:10:24Z 2021 Final Year Project (FYP) Ho, J. (2021). Deep-learning in survival analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148490 https://hdl.handle.net/10356/148490 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Mathematics::Statistics
spellingShingle Science::Mathematics::Statistics
Ho, Jeff
Deep-learning in survival analysis
description In this paper, hospitalisation duration is modelled using traditional survival model, machine-learning models and deep-learning models. Machine-learning and deep-learning algorithms typically assume that all event of interest are known at the time of modelling. However, hospitalisation duration is a time-to-event data with right-censoring (not all events are known at the time of modelling). Hence, specific techniques were employed to deal with this inconsistency. Subsequently, the various models are evaluated using the Concordance-index (C-index). It is a ranking evaluation metrics that can account for censored observation. The empirical results showed that the deep-learning model is best in predicting hospitalisation despite the small dataset. This paper can be further improved by incorporating geo-spatial data in the analysis.
author2 Xiang Liming
author_facet Xiang Liming
Ho, Jeff
format Final Year Project
author Ho, Jeff
author_sort Ho, Jeff
title Deep-learning in survival analysis
title_short Deep-learning in survival analysis
title_full Deep-learning in survival analysis
title_fullStr Deep-learning in survival analysis
title_full_unstemmed Deep-learning in survival analysis
title_sort deep-learning in survival analysis
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148490
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